The present invention relates to rib centerline line extraction in 3D medical image data, such as 3D computed tomography (CT) scans, and more particularly to automatic rib centerline extraction using learning based deformable template matching.
Locating rib metastases and fractures in chest CT scans typically involves reading hundreds of axial CT slices to visually track changes in rib cross-section area. Accordingly, manual reading of CT scans is rather time consuming and rib anomalies are frequently missed in practice due to human error. Automatic extraction of rib anatomical centerlines can be used to enhance the visualization of an unfolded rib cage, which can make routine bone reading tasks more efficient and effective for the radiologists. Extracted and labeled rib centerlines can also serve as a reference to localize organs, register pathologies, and guide correspondence between serial thoracic CT scans for interval change analysis. In addition, the derived information of the rib geometry can assist with the rib cage fracture fixation surgeries.
Despite the above described clinical importance, automatic detection and labeling of ribs in CT scans remains a challenging task. Most conventional methods model the ribs as elongated tubular structures and employ Hessian or structure tensor eigen-system analysis for ridge voxel detection. However, these algorithms are typically computationally expensive and may not achieve consistent results in all patients. FIG. 1 illustrates examples of different rib cross sections in different patients. As shown in FIG. 1, image (b) shows rib cross sections 104 having more clear dark marrow than the rib cross-sections 102 shown in image (a). In many cases, the rib marrow may be darker than the rib boundary; thus the rib center points may not be consistently detected as ridge voxels. To construct a rib centerline, tracking based methods, such as Kalman filtering, are typically used to trace detected rib center points from one slice to the next. However, some conventional tracking based methods require manual initial seed points, and such point to point tracking methods are highly sensitive to local ambiguities or discontinuities posed by rib pathologies like fractures, which may be of the most interest to radiologists. FIG. 2 illustrates examples of different rib pathologies, which can lead to inaccurate rib centerlines in tracing bade methods. As illustrated in FIG. 2, image (a) shows ribs with missing rib segments 202 and image (b) shows ribs with rib metastases 204. Furthermore, in convention rib tracing algorithms, each rib is individually detected and traced; hence rib labeling requires a separate heuristic method.